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Office of Energy Efficiency & Renewable Energy

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Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Estimating the Value of UtilityScale Solar Technologies in 

California Under a 40% 

Renewable Portfolio Standard

J. Jorgenson, P. Denholm, and M. Mehos

Technical Report

NREL/TP-6A20-61685

May 2014
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Estimating the Value of UtilityScale Solar Technologies in 

California Under a 40% 

Renewable Portfolio Standard

J. Jorgenson, P. Denholm, and M. Mehos

Prepared under Task No. SS13.3060

Technical Report

NREL/TP-6A20-61685

May 2014
NOTICE

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or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
iii

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Acknowledgments

This project was funded by the U.S. Department of Energy Solar Energy Technologies Program. 

The following individuals provided valuable input during the analysis and publication process: 

Adam Green, Frank Wilkins, Kelly Eurek, Eduardo Ibanez, Jenny Melius, Joshua Eichman, 

Craig Turchi, David Schlosberg, Shucheng Liu, Udi Helman, Aaron Bloom, and Maggie Mann.

Any errors or omissions are the sole responsibility of the authors.
iv

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List of Acronyms

CAISO California Independent System Operator

CO2 carbon dioxide

CPUC California Public Utilities Commission 

CSP concentrating solar power

ELCC effective load-carrying capability

IID Imperial Irrigation District

LDWP Los Angeles Department of Water and Power

LOLP loss-of-load probability

Munis local municipal and cooperative utilities

NREL National Renewable Energy Laboratory

PG&E Pacific Gas and Electric

PNNL Pacific Northwest National Laboratory

PV photovoltaic

REPRA Renewable Energy Probabilistic Resource Adequacy 

tool

RPS renewable portfolio standard

SAM System Advisor Model

SCE Southern California Edison

SDG&E San Diego Gas & Electric

SM solar multiple

SMUD Sacramento Municipal Utility District

TES thermal energy storage

TIDC Turlock Irrigation District

VO&M variable operation and maintenance 

WWSIS-2 The Western Wind and Solar Integration Study: Phase 2
v

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Executive Summary

This analysis is follow-on work to a previous study of the value of concentrating solar power 

(CSP) with thermal energy storage (TES) in the state of California. As in the previous study, it 

analyzes the value of both CSP-TES and utility-scale photovoltaics (PV), with the following 

additions:

1. This analysis uses the October 2013 database of the California grid as prepared by the 

California Independent System Operator (CAISO). This version of the database includes 

reductions in fuel costs and prices of carbon dioxide emissions, among other updates.

2. In the base case, this analysis assumes that California adds 1,175 MW of energy storage 

to reflect policy targets. This analysis also evaluates sensitivities to this assumption.

3. This analysis evaluates both the base 33% renewable portfolio standard (RPS) released 

by the CAISO, and a 40% RPS scenario created by NREL. The additional renewables are 

based on an assumed 75%/25% split between solar PV and wind, with no changes to the 

non-RPS resources.

4. This analysis considers multiple CSP-TES configurations, starting with a base 

assumption of a solar multiple (SM) equal to 1.3 with 6 hours of storage but also with 

solar multiples up to 2.7 with 15 hours of storage.

5. As in the previous study, the base case considers no export limits from California to 

neighboring states beyond inherent transmission capacity. However, this analysis also 

considers the impact of restricting California net exports to 0 MW and 1,500 MW.

This analysis includes two sources of value for either solar technology: operational value and 

capacity value. Operational value represents the avoided costs of conventional generation, which 

includes fuel costs, start-up costs, variable operation and maintenance costs, and emission costs. 

Capacity value reflects the ability of PV or CSP-TES to avoid the costs of building new 

conventional thermal generators in systems that need capacity in response to growing energy 

demand or plant retirements. Table ES-1 quantifies the operational value of CSP-TES and PV, 

and compares the potential value of providing firm system capacity.

Table ES-1. Total Value (Operational Value Plus Capacity Value Range) in Two RPS Scenarios

Value Component

33% RPS 40% RPS

CSP-TES 

Value ($/MWh) PV Value ($/MWh) CSP-TES Value ($/MWh) PV Value ($/MWh)

Operational 46.6 31.9 46.2 29.8

Capacity 47.9–60.8 15.2–26.3 49.8–63.1 2.4–17.6

Total 94.6–107 47.1–58.2 96.0–109 32.2–47.4

The totals in Table ES-1 represent the highest value configurations for CSP-TES plants. Larger 

CSP-TES solar multiples tend to reduce the average value because more energy is produced 

during times of lower value, resulting in a flatter output shape. Reducing export capacity changes 

the value of CSP-TES very little but somewhat increases PV curtailment, slightly lowering the

PV system value. Eliminating 1,175 MW of energy storage increases the relative flexibility 

benefits of dispatchable CSP-TES and results in higher value.
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Table ES-1 also indicates that a large fraction of the value of CSP-TES appears to be derived 

from its ability to provide firm system capacity. The range of values is based on significant 

uncertainty of the capacity value of PV at increased renewable energy penetration as well as a 

range of estimates of the cost of new conventional thermal capacity. More robust estimates of the 

impact of increased penetration of PV on its capacity value are needed. The capacity value 

analysis shows an interaction between the capacity value of PV and CSP that warrants further 

analysis. 

Additionally, while this analysis provides insight into the value of CSP-TES providing grid 

flexibility it does not examine the role of such flexibility in enabling greater penetration of wind 

and solar PV. Further analysis is needed to examine the portfolio benefits of CSP in meeting 

higher penetrations of variable generation resources. 

Finally, this analysis compares only the relative value of CSP-TES in various situations and 

configurations and does not indicate the capital cost of building a plant. Further analysis will 

examine the trade-off between CSP-TES capital costs and total value provided to the power 

system.
vii

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Table of Contents

List of Figures .......................................................................................................................................... viii

List of Tables ............................................................................................................................................ viii

1 Introduction........................................................................................................................................... 1

2 Simulating CSP in a Production Cost Model..................................................................................... 2

2.1 CAISO Database for PLEXOS ........................................................................................................ 2

2.2 Incremental PV and CSP-TES Generation in the 33% RPS Case ................................................... 6

3 Implementation and Discussion of 40% RPS .................................................................................. 10

3.1 Integration of Incremental PV or CSP-TES................................................................................... 13

3.2 Constraining CAISO Exports ........................................................................................................ 15

3.3 Sensitivity to Existing Energy Storage .......................................................................................... 16

4 Impact of Plant Configuration on Value of CSP-TES...................................................................... 19

5 Impact of Capacity on the Overall Value of CSP-TES..................................................................... 23

6 Conclusions ........................................................................................................................................ 27

7 References .......................................................................................................................................... 28

Appendix A: Changes in CSP Value Relative to the 2013 CSP Value Study ...................................... 31

Appendix B: How PV Can Increase the Capacity Value of CSP-TES................................................... 32
viii

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List of Figures

Figure 1. Map of the California study area. .............................................................................................. 3

Figure 2. Dispatch of CAISO generator fleet for two days in spring, summer, and winter in the 33% 

RPS case. The black line shows total load. ....................................................................................... 6

Figure 3. Three major operations within a CSP-TES plant: the solar field and receiver, the thermal 

storage tank, and the power block. .................................................................................................... 7

Figure 4. Process of simulating CSP in a production cost model......................................................... 8

Figure 5. Annual generation from RPS sources for the base 33% RPS case and the scaled 39% 

RPS ...................................................................................................................................................... 10

Figure 6. The hourly duration curve for net CAISO inflows in the 33% RPS case compared to the 

39% case. Negative inflows represent energy exported out of CAISO......................................... 12

Figure 7. Displaced energy in the Western Interconnection because of an increase from 33% RPS 

energy to 39% RPS energy in CAISO ............................................................................................... 13

Figure 8. Difference in displaced generation for PV and CSP-TES in the 40% RPS case................. 14

Figure 9. Net imports (representing load minus generation in CAISO) for the 33% RPS case, 39% 

RPS case, and the cases with added PV or CSP-TES to reach 40%............................................. 15

Figure 10. The operational value of a CSP-TES and PV plant added to a system with varying 

configurations of existing storage.................................................................................................... 18

Figure 11. Impact of the solar multiple parameter on the energy flow of a CSP plant...................... 19

Figure 12. Marginal operational value of tower CSP-TES plants in the 40% RPS scenario with 

varying configurations....................................................................................................................... 20

Figure 13. Average daily marginal energy prices for three major seasons in the 40% RPS scenario 

in CAISO .............................................................................................................................................. 21

Figure 14. Average daily dispatch of a tower CSP-TES plant in a CAISO 40% RPS scenario with a 

SM of 1.3 and with 0, 3, and 6 hours of TES .................................................................................... 21

Figure 15. Average daily dispatch of a tower CSP-TES plant in CAISO with 6 hours of TES capacity 

and SMs of 1.3, 1.7, and 2.0............................................................................................................... 22

Figure 16. Breakdown of the limitations of incoming solar energy for solar multiples varying 

(across) from 1.3 to 2.7 with increasing amounts of thermal storage .......................................... 22

Figure 17. Total value range, which includes operational and capacity value, of CSP-TES and PV in 

two RPS scenarios ............................................................................................................................. 25

Figure 18. Total operational and capacity value of several configurations of CSP-TES in the 40% 

RPS scenario....................................................................................................................................... 26

Figure A-1. Change in peak demand period resulting from the deployment of PV on July 22......... 32

List of Tables

Table ES-1. Total Value (Operational Value Plus Capacity Value Range) in Two RPS Scenarios...... v

Table 1. Characterization of the Regions Modeled in the CAISO Database ......................................... 4

Table 2. Description of the Installed Capacity of the CAISO Region in the 33% RPS Scenario......... 5

Table 3: Description of the Reserve Requirements Enforced in the CAISO 33% Database ............... 6

Table 4. Marginal Operational Value of Two Solar Technologies in the 33% RPS Case in CAISO .... 9

Table 5. Avoided Fuel by Generator Type per Unit of Energy in the 33% RPS Case........................... 9

Table 3. Annual Generation From RPS and non-RPS Renewable Energy Sources for the 33% RPS 

and the Scaled 39% RPS in Gigawatt Hours Before Incremental PV or CSP-TES....................... 10

Table 6. The Application of the Scaled CAISO Method Using the Incremental WWSIS-2 Reserves 

Methodology to the Reserves Products Modeled in the CAISO Database................................... 11

Table 7. Characterization of the Regions Modeled in the Scaled 39% Case ...................................... 12

Table 8. Marginal Operational Value From a CSP-TES Plant and a PV Plant Implemented in CAISO 

40% RPS Case..................................................................................................................................... 14

Table 9. Marginal Operational Value of PV and CSP-TES in a 40% RPS Case With Export 

Limitations........................................................................................................................................... 16

Table 10. Annual Operational Benefit of Two Types of Storage Evaluated in the 39% RPS Scenario17

Table 11. Relative Value of CSP-TES Compared to PV Under Various Assumptions Regarding 

Existing Storage ................................................................................................................................. 18
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Table 12. The SMs and Corresponding Plant Characteristics of CSP-TES Plants Modeled............. 20

Table 13. Total Value (Operational Value Plus Capacity Value) Range in Two RPS Scenarios........ 25

Table 14. Capacity Credit Assigned to Various Configurations of CSP-TES Plants in the 40% RPS 

Scenario Using Two Methods ........................................................................................................... 26

Table A-1. Sources of Value Reduction in This Study Compared to a Previous NREL Study of CSP 

in CAISO .............................................................................................................................................. 31
1

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1 Introduction

With growing interest in renewable energy, the penetration of solar photovoltaics (PV) and wind 

generation is likely to rise. Accommodating the time-varying and uncertain output of these 

resources will be a challenging aspect of integrating large-scale renewable energy into the 

electric power system. Concentrating solar power (CSP) with thermal energy storage (TES) is a 

unique source of solar energy in that its output can be shifted over time and also controlled in 

response to system operator signals, allowing for provision of a wide range of grid services. The 

ability of CSP-TES to be a flexible source of renewable generation may be particularly valuable 

in regions with high overall penetration of solar energy, such as the state of California. 

California’s renewable portfolio standard (RPS) requires the state to increase electricity 

generation from eligible renewable energy resources to reach at least 33% of total retail sales of 

electricity per year by December 31, 2020. Beyond 2020, California targets a further reduction in 

greenhouse gas emissions to 80% of 1990 emission levels by the year 2050 (Brown 2013). To 

help reach this ambitious goal, current California governor Jerry Brown has stated that a higher 

40% RPS might be reachable in the near term (Brown 2011). 

In this report, we evaluate the value of adding an additional PV or CSP-TES plant to the base 

case 33% RPS scenario using a grid model developed by the California state energy agencies and 

maintained by the California Independent System Operator (CAISO). To examine changes in 

value at higher levels of penetration, we also assess the value of both PV and CSP in a 40% RPS 

scenario. We compare the technical and economic aspects of both solar technologies under 

various constraints and sensitivities, including limits on exporting renewable energy from 

California to surrounding regions, as well as the configuration of the CSP plant. 
2

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2 Simulating CSP in a Production Cost Model

2.1 CAISO Database for PLEXOS

The modeling framework utilized in this study is based on the one developed by the California 

state agencies for 10-year ahead resource planning in jurisdictional utilities. CAISO produces

and maintains a database for PLEXOS, a commercially-available production cost modeling 

software used to simulate the power grid in California for planning purposes. Production cost 

modeling software allows grid planners and operators to assess many aspects of power 

generation, including system costs, reliability, and emissions. The database contains generatorlevel details of California’s electricity sector as well the rest of the Western Interconnection

because California is highly interconnected with several western states and historically imports a 

significant amount of power from neighboring regions. CAISO has utilized the database in 

analyses of the original 20% RPS as well as the 33% renewable integration study for the 

California Public Utilities Commission (CPUC) (CAISO 2011 and CAISO 2012).

Production cost models such as PLEXOS are formulated to minimize the total cost of generating 

electricity to serve energy demand in every time step by committing (determining whether a 

generator is on or off) and dispatching (adjusting the output of the committed generators). 

However, unit commitment and dispatch is complicated by many system constraints. For 

example, balancing authorities in the United States must maintain a given level of operating 

reserves. In functional power systems, reserves ensure reliable power delivery in case of rapid 

changes in energy supply or demand. As modeled, operating reserves represent partially-loaded 

generators with the ability and necessary capacity to change their output. In addition, the model 

has simplified transmission constraints that enforce the minimum and maximum allowable 

power flow into or out of a region. Finally, the model specifies constraints on the individual 

generators, which include ramp rates, minimum generation levels, outage profiles, heat rates, and 

many more. 

Table 1 describes the three aggregated regions represented in the model. For consistency with the 

database, the CAISO footprint is modeled as the area served by the three largest investor-owned 

utilities in the state of California: Southern California Edison (SCE), San Diego Gas and Electric 

(SDG&E), and Pacific Gas and Electric (PG&E). This represents approximately 80% of the total 

load in the state, as shown in Table 1, with the remainder served by local municipal and 

cooperative utilities (Munis), which include the Los Angeles Department of Water and Power 

(LDWP), Imperial Irrigation District (IID), Turlock Irrigation District (TIDC), and Sacramento 

Municipal Utility District (SMUD). Figure 1 provides a map of the study area, including the 

location of the added CSP plant discussed in Section 2.2.
3

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Figure 1. Map of the California study area.1

The base case formulation includes the RPS generation necessary to exceed the 33% RPS 

mandate for 2020 given the forecasted load for that year. Overall, the total RPS energy in the 

database (87.8 TWh) represents approximately 34.6% of CAISO’s annual demand, or 

approximately 28% of California’s total annual demand2. The model includes generator-level 

 

1 This map is used only as an illustration. The boundaries of the utility service territories are approximate.

2 The CPUC calculates renewable energy to meet the 33% RPS goal based on retail sales, which includes 

transmission and distribution losses, not the modeled load.
4

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detail for the entire Western Interconnection and solves an optimal least-cost dispatch for the 

entire geography, with defined flow limits and wheeling charges for interchanges between 

regions. In reality, many individual utilities outside CAISO operate their power systems 

individually, subject to various agreements with their neighbors, and hence the actual dispatch 

may not be least-cost for the region.

Table 1. Characterization of the Regions Modeled in the Database

2022 CAISO Munis Rest of the West

Peak Load (GW) 54.3 12.0 122.9

Annual Demand (TWh) 254 58.0 753

CA RPS Energy (TWh) 60.4 9.2 18.2

Other Wind and Solar 

Energy (TWh)3 6.4 0 69.4

Annual Net Imports 

(TWh) 46.24 29.6 -75.8

Areas Served/

Balanced By

SCE, SDG&E, 

PG&E

LDWP, IID, 

TIDC, SMUD

Rest of the West (includes areas in 

CO, AZ, WY, NM, AZ, WA, OR, NV, ID, UT, 

MT, Alberta, and British Colombia)

Table 2 describes the installed capacity in the CAISO region for the base 33% RPS scenario, 

with the RPS-eligible generators separated. The assumed generation-averaged fuel prices are 

$1.71/MMBtu for coal and $4.38/MMBtu for natural gas. The fuel prices vary by region, and the 

natural gas prices also vary by month to reflect seasonal demand. In addition, carbon dioxide 

(CO2) emissions within California cost $21.9/ton. Carbon dioxide emissions outside of 

California have no modeled cost, though energy imported to California has a small added cost to 

reflect the incoming cost of carbon.

 

3 This category contains California generation that does not satisfy the RPS (e.g., 6.4 TWh of distributed PV in 

CAISO) or wind and solar in other states that may satisfy other RPSs, such as Arizona’s. 4 For comparison, in 2012 the state of California (CAISO plus Munis) imported approximately one-third of its total 

annual demand (CEC 2014).
5

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Table 2. Description of the Installed Capacity of the CAISO Region in the 33% RPS Scenario

Capacity Installed (MW)

Non RPS5 RPS6

Biomass - 1,080

Coal 138 -

CSP7 - 1,400

Demand Response 2,730 -

Gas Combined-Cycle 17,900 -

Gas Combustion Turbine 7,430 -

Geothermal - 2,460

Hydropower 7,350 1,380

Nuclear 2,240 -

PV 2,080 9,950

Steam/Other 615 -

Storage 3,025 -

Wind - 10,400

Total 42,300 26,600

We use the model discussed above as a starting point for the analysis, but we include one major 

deviation, shown in Table 2. The 3,025 MW of CAISO storage shown above includes 1,850 MW 

of existing pumped hydropower storage as well as an assumed additional 1,175 MW of battery

storage. This addition represents the procurement of energy storage mandated by the CPUC by 

the end of 2020 in accordance with Rulemaking R.10-12-007 (CPUC 2013). Although the 

rulemaking requires the procurement of 1,325 MW, 150 MW of this storage is already reflected 

in the database as a CSP plant. The additional 1,175 MW of storage is divided among the three 

investor-owned utilities as detailed in R.10-12-007 and represents a long-duration battery with 8 

hours of energy storage capacity and a round-trip efficiency of 70%. In reality, short-duration 

storage may largely comprise the new storage procurement, thus Section 3.3 provides additional 

details and addresses alternatives to this assumption.

In addition to committing and dispatching the generators shown in Table 2, the model also 

ensures a supply of operating reserves. In functional power systems, reserves ensure reliable 

power delivery in case of changes in energy supply or demand. As modeled, operating reserves 

represent committed generators with the ability and necessary capacity to change their output 

upward or downward. Table 3 describes each of the six types of operating reserves enforced in 

the model. The table describes the name of the product, magnitude of the requirement, and the 

amount of time it takes for each generator to respond. The CAISO used a tool developed by the 

 

5 Only small hydropower plants contribute to the RPS, which means that a significant portion of hydropower is not 

eligible. In addition, distributed demand-side PV is not eligible for RPS status. 6 The RPS category also includes out-of-state generators that contribute to the RPS procurement. Specifically, 183 

MW of biomass capacity, 215 MW of geothermal capacity, 7 MW of hydroelectric capacity, 486 MW of PV 

capacity, and 4,180 MW of wind capacity are located out of state. The overall penetration of PV in CAISO, 

including non-RPS PV, is 10.9%. 7 This represents three existing solar thermal generators in the database. Two of them have fixed output with no 

storage. The third has six hours of storage and a maximum capacity of 150 MW. The representation of the 150-MW 

plant allows for the co-optimization of dispatch and operating reserves, but its characterization is much simpler than 

that of the CSP-TES plant modeled here, as discussed in Section 2.2.
6

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Pacific Northwest National Laboratory (PNNL) to calculate the regulation and load-following 

requirements for the 33% RPS database (Makarov et al. 2010).

Table 3: Description of the Reserve Requirements Enforced in the Database

Reserve Product Time to 

Respond

Annual Req. 

(GW-h)

Mean Hourly 

Req. (MW-h)

Load-Following Up (LFU) 10 min. 10,960 1,254

Load-Following Down (LFD) 10 min. 10,790 1,235

Regulation Up 5 min. 3,700 424

Regulation Down 5 min. 3,800 440

Spinning Contingency 5 min. 7,500 860

Non-Spinning Contingency 5 min. 7,500 860

Figure 2 shows the hourly dispatch of the generators located physically within the CAISO region

for two contiguous days in spring, summer, and winter for the base 33% RPS case. The 

difference between the total CAISO load, shown by the black line, and CAISO generation 

represents energy that must be imported from outside the region. In this 33% case, CAISO is still 

a net importer of energy for almost every hour of the year. However, excess generation 

associated with high solar output in the spring and early summer combined with relatively low 

load causes CAISO to export energy 61 hours of the year in the amount of 35 GWh (only 0.02% 

of energy produced within CAISO), with a maximum hourly flow of 1,730 MW exported power.

Because of the technical and economic implications of exporting energy out of CAISO, in 

Section 3.2 we evaluate scenarios with constrained export capabilities.

Figure 2. Dispatch of CAISO generator fleet for two days in spring, summer, and winter in the 33% 

RPS case. The black line shows total load.

2.2 Incremental PV and CSP-TES Generation in the 33% RPS Case

In this analysis, we first compare the system above with and without the addition of incremental

PV or CSP-TES with equal annual energy. Denholm et al. provided a detailed report on adding 
7

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incremental PV or CSP-TES to an earlier version of this 33% RPS system (2013). For 

comparison, we first evaluate the value of incremental PV or CSP-TES in the current 33% RPS 

system before implementing the 40% RPS scenario. We calculate the difference in total 

production cost (which includes fuel costs, variable operation and maintenance, or VO&M,

costs, and start-up costs) between the two runs and attribute the marginal system savings to the 

new solar plant. To correctly capture the dispatch and value of CSP-TES, we must integrate and 

connect the three aspects of a solar thermal plant to the storage depicted in Figure 3: the solar 

field and receiver, the storage tank, and the power block. Jorgenson et al. (2013) describe the 

methodology in detail.

Figure 3. Three major operations within a CSP-TES plant: the solar field and receiver, the thermal 

storage tank, and the power block.

Firstly, we quantify the solar resource of a typical good solar resource location in California. 

Meteorological data for the site chosen, located near Daggett, California, originates from the 

National Solar Radiation Database for the year 2006. This data is an input to the National 

Renewable Energy Laboratory’s (NREL’s) System Advisor Model (SAM) version 2013-1-15 

(Gilman et al. 2008; Gilman and Dobos 2012). The molten salt power tower model within SAM 

converts hourly irradiance into thermal energy and then into net electrical energy based on the 

rated gross thermal-to-electric efficiency of the dry-cooled turbine. Downtime, outages, start-up 

energy, and part-load efficiency decrements were neglected in SAM to be taken into account 

during dispatch modeling. The electrical equivalent energy then served as a dispatchable 

resource in PLEXOS within the constraints of the thermal power block characteristics of the 

CSP-TES plant discussed by Jorgenson et al. (2013). Figure 4 describes the modeling flow. 
8

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Figure 4. Process of simulating CSP in a production cost model

The “raw” electrical energy flow from SAM is applied to a modified hydropower algorithm in 

PLEXOS to simulate dispatched storage and CSP-TES generator operation. Specifically, in each 

hour the model can send electrical energy into storage, immediately into the grid through the 

CSP power block, or a combination of the two. The model can also choose to draw energy from 

storage to feed the power block. This framework accounts for three types of losses within the 

plant: start-up losses incurred when warming up the plant, part-load efficiency losses resulting 

from operating below the design point, and storage losses that result from transferring and 

storing heat. In addition, the plant has operational parameters, such as a ramp rate and minimum 

generation level similar to conventional steam turbine generators. Incorporating the CSP-TES 

plant into PLEXOS allows the model to optimally schedule both the output of solar energy and 

co-optimize the plant’s capacity to provide operating reserves. Previous findings indicate that 

reducing the ratio of the solar collection field size to the power block capacity (a design 

parameter often called the SM) in combination with sufficient thermal storage provides the most 

benefit to the power system per unit of energy produced (Jorgenson et al. 2013).

8 Thus, we use a 

plant that has an SM equal to 1.3 and enough thermal storage to operate the plant at rated 

capacity for six hours. Section 4 addresses the sensitivity to these configuration parameters.

Adding the same annual energy output from PV and CSP-TES (approximately 3,400 GWh, 

which represents an approximate increase of 1% solar penetration relative to total load) requires

1,172 MW of CSP-TES and 1,576 MW of PV. The new PV capacity was implemented by 

linearly scaling existing RPS PV located in existing locations above the 33% case. After 

implementing either solar technology into the model, we observe a reduction in overall total 

production cost and attribute the savings to the addition of the incremental PV generation or the 

new CSP-TES plant. Total production costs fall into four categories: fuel costs, VO&M, start-up 

costs, and emissions costs. Table 4 shows the breakdown of these cost savings. In this 33% case, 

the addition of CSP-TES results in an additional reduction of $14.7 per MWh of energy 

delivered compared to the addition of PV. The difference in displaced fuel costs between these 

two technologies results from the ability of CSP-TES to shift solar energy to replace the more

costly generators, which run on natural gas, shown in Table 5. The operational value of CSPTES, $46.6/MWh, is lower than previously reported values using earlier versions of the same 

database. Previously, Denholm et al. reported an operational value of $83/MWh (2013). 

Appendix A details the sources of this deviation. 

 

8 This finding only indicates a higher value provided to the power system in terms of reduced operational costs of 

conventional thermal generators, not necessarily reduced capital costs.
9

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Table 4. Marginal Operational Value of Two Solar Technologies Added to the 33% RPS Case in 

California

Marginal Operational Value in 33% RPS Case ($/MWh)

CSP-TES (SM = 1.3, 6 hrs TES) PV

VO&M 1.6 1.2

Start-up and Shut-down 2.5 -0.9

Fuel 34.4 27.9

Emissions 8.1 3.7

Total 46.6 31.9

Table 5. Avoided Fuel by Generator Type per Unit of Energy in the 33% RPS Case

Avoided Fuel per Unit Energy (MMBTu/MWh)

CSP-TES (SM = 1.3, 6 hrs TES) PV

Biomass 0.2 0.5

Coal 0.3 0.9

Gas Combined Cycle 6.8 6.0

Gas Combustion Turbine 0.7 0.1

Total 7.9 7.5
10

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3 Implementation and Discussion of 40% RPS

Next we assess a scenario in which PV or CSP is added to a 39% portfolio, resulting in a total 

renewable penetration of approximately 40% within CAISO. To create a 39% scenario, we scale 

the hourly profiles of new wind and PV RPS generators in the 33% scenario with an incremental 

ratio of 25% wind to 75% PV in the assumed existing locations. This ratio reflects various 

incentives within California to promote distributed PV generation from the California Energy 

Commission (2011) and increases the overall PV penetration (including RPS and non-RPS PV) 

in CAISO from 10.9% to 14.1%. Figure 5 and Table 3 show the breakdown of RPS energy for 

both the original 33% and the scaled 39% case before the addition of incremental CSP-TES or 

PV.

Table 3. Annual Generation From RPS and non-RPS Renewable Energy Sources for the 33% RPS 

and the 39% RPS in Gigawatt Hours Before Incremental PV or CSP-TES 

GWh 33% RPS 39% RPS

Biomass 9,400 9,400

Geothermal 21,500 21,500

Small Hydropower 7,200 7,200

Wind 25,100 27,800

PV 21,200 29,300

CSP 3,400 3,400

Total RPS 87,500 98,600

Non-RPS PV 6,400 6,400

Total RE 94,200 105,000

Figure 5. Annual generation from RPS sources for the base 33% RPS case and the 39% RPS

Integrating these additional variable generation resources incurs a change in operating reserve 

requirements. We do not have access to the PNNL/CAISO methodology for calculating these 

new reserves requirements, so we evaluate an alternate method utilized in the Western Wind and 

Solar Integration Study: Phase 2 (WWSIS-2) (Ibanez et al. 2012, Lew et al. 2013). The 

calculation assumes the procurement of adequate reserves to meet 70% of forecast errors in the 

1-hour time frame and 95% of 10-minute forecast errors. The additional reserves are calculated 

based on the difference between these forecast errors in the 39% and 33% cases and then added 

to the original reserve requirements in the 33% RPS database. The results of this method are 
11

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shown in Table 6. Note that the reserve requirements increase only slightly, by approximately

1%, compared to those shown Table 3. The reserves requirements of wind and solar energy have

not been definitively determined and are an area of active research. Further analysis will be 

required to understand the impact of wind and solar on both regulation and load-following or 

flexibility reserves requirements and associated system costs. Spinning and non-spinning 

reserves, which are defined as 3% of hourly load, do not change.

Table 6. The Application of Incremental WWSIS-2 Reserves Methodology to the Reserves 

Products Modeled in the Database

Reserve Product Time to Respond Annual Req. 

(GW-h)

Mean Hourly 

Req. (MW-h)

Load-Following Up (LFU) 10 min. 11,027 1262

Load-Following Down (LFD) 10 min. 10,853 1242

Regulation Up 5 min. 3,748 429

Regulation Down 5 min. 3,878 443

Spinning Contingency 5 min. 7,500 860

Non-Spinning Contingency 5 min. 7,500 860

After implementing the additional PV and wind resources as well as the new reserve profiles, we 

observe some significant operational changes to the system, shown regionally in Table 7. First, 

the higher levels of RPS generation within CAISO lead to lower levels of energy imports from 

surrounding regions. In addition, the number of hours in which CAISO is a net energy exporter 

increases from 61 hours in the 33% RPS case to 342 hours in the 39% RPS case. The amount of 

energy exported increases from 35 GWh to 313 GWh (approximately 0.2% of total energy 

generated within CAISO), with an hourly maximum of 3,240 MW exported. Figure 6 shows the 

duration curve of hourly CAISO inflows throughout the year. Negative inflows represent a net 

flow of energy from CAISO into the neighboring regions. Figure 7 indicates the location and 

quantity of generation displaced by the new RPS generation. Although most RPS generation is 

located within the boundaries of CAISO, a reduction of out-of-state imports means that displaced 

generation is largely located outside of CAISO in regions such as the Pacific Northwest and 

Arizona. The additional wind and solar largely displaces gas combined-cycle generation, which 

is typically the marginal resource in much of the Western United States. The assumed ability of 

the neighboring regions to accept this new RPS energy and the modeled flexibility of these 

generators lead to very low curtailment (or overgeneration) in the 39% scenario. Approximately

0.01% of the incremental RPS energy added to the 33% case to reach the higher RPS is 

curtailed.

9

This curtailment rate is lower than shown in previous analysis of a CAISO 40% RPS 

scenario and driven largely by the different mix of wind and solar (Energy and Environmental 

Economics, Inc. 2014). Previous analysis has shown that curtailment rates of solar are highly 

nonlinear, which demonstrates the need to evaluate many scenarios of renewable penetration to 

determine the relative value of different generation mixes (Denholm and Mehos 2011).10

 

9 In this document, we use the term curtailment to represent energy from renewables that could have been delivered 

to the grid (i.e., the resource was available) but was not delivered because of any system constraint. These 

constraints include generator flexibility, low demand, and regional transmission limits. 10 For example, in this study the penetration of solar PV was approximately 14% on an energy basis, whereas the 

Energy and Environmental Economics study used a PV penetration of approximately 20% (2014). Previous analysis 
12

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Table 7. Characterization of the Regions Modeled in the Scaled 39% Case

2022 CAISO Munis WECC

Peak Load (GW) 54.3 12.0 122.9

Annual Demand (TWh)

(% of total Western 

Interconnection)

254

(23.8%)

57.9

(5.4%)

754

(70.7%)

CA RPS Energy (TWh)

(% of total CA RPS)

68.9

(69.8%)

10.1

(10.2%)

19.7

(20.0%)

Other Wind and Solar Energy (TWh) 6.4 0 69.4

Net Annual Imports (TWh) 41.2 29.8 -71.0

Incremental CA RPS Energy (TWh) 8.5 0.9 1.5

% of CA RPS Generation 

Displaced in Region (%) 32.7 11.5 55.8

Figure 6. The hourly duration curve for net CAISO inflows in the 33% RPS case compared to the 

39% case. Negative inflows represent energy exported out of CAISO.

 

has demonstrated that this relatively small difference can produce a difference of more than 20 percentage points in 

the marginal curtailment rate (Denholm and Mehos 2011). In addition, this analysis makes different assumptions 

about energy storage capacity, generator must-run status, and export limitations.
13

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Figure 7. Displaced energy in the Western Interconnection as a result of an increase from 33% 

RPS energy to 39% RPS energy in CAISO

3.1 Integration of Incremental PV or CSP-TES

Using the same methodology presented in Section 2.2, we first compare the 39% system 

described above to a system with and without the addition of this incremental PV or CSP-TES

(to achieve a 40% RPS) and attribute the cost savings to the new plant. Adding the same annual 

energy output from PV and CSP-TES (approximately 3,400 GWh) requires 1,172 MW of CSPTES and 1,576 MW of PV,

11 as before. 

Table 8 shows the change in total production costs, normalized by the total MWh of energy 

produced by either plant. First, the value of both CSP-TES and PV declines slightly in the 40% 

scenario because of the presence of new zero-marginal-cost generation (PV and wind). However, 

CSP-TES avoids $16.4/MWh more than PV, which is slightly higher than the $14.7/MWh in the 

33% case. Thus, the value of PV declines at a faster rate than the value of CSP-TES. 

Additionally, as in the 33% case, the CSP-TES plant displaces roughly the same quantity of CO2

emissions as PV, but avoids more emissions costs. As discussed further below, this results from 

the fact that CO2 has a cost of $22/ton inside California but $0/ton in the rest of West, indicating

that the PV plant is displacing generation largely outside California.

 

11 The incremental PV in this 40% RPS case resulted in very low marginal curtailment (0.1%).
14

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Table 8. Marginal Operational Value From a CSP-TES Plant and a PV Plant Implemented in a 40% 

RPS Case

Marginal Operational Value in 33% RPS Case ($/MWh)

CSP-TES (SM = 1.3, 6 hrs TES) PV

VO&M 1.5 1.2

Start-up and Shut-down 2.9 -0.4

Fuel 33.9 25.8

Emissions 7.9 3.2

Total 46.2 29.8

Figure 8 shows the difference in displaced generation for PV and CSP-TES. Both plants displace 

the same amount of energy, but the majority of the CSP-TES generation (60%) displaces energy 

within CAISO, whereas only 30% of the generation from the PV plant displaces other CAISO 

generation. Adding incremental PV generation also leads to an increase in utilization of storage 

within CAISO. Output from all existing storage devices increases slightly in the PV case, but the 

co-optimized flexible battery output increases by 16% overall. This indicates that the cooptimized battery is using more of its capacity to shift energy instead of to providing reserves.

12

The importance of the assumed flexibility and configuration of the battery is evaluated in Section 

3.3.

Figure 8. Difference in displaced generation for PV and CSP-TES in the 40% RPS case

As shown in Figure 8, a reduction in conventional generation due to the presence of additional 

solar energy leads to a reduction in CO2 emissions. Although both technologies displace a 

similar amount of emissions per MWh produced (484 lbs./MWh for CSP-TES and 507 

lbs./MWh for PV)13, CSP-TES displaces more emissions within CAISO compared to PV. This 

 

12 Increased battery output results in increased charging demand as well, meaning that overall system load increases. 13 PV displaces slightly higher amounts of CO2 per MWh produced since it displaces a higher proportion of coal 

generation, shown in Figure 8.
15

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causes CSP-TES to displace more than twice the emissions cost than does PV, since CO2 outside 

of California has no modeled cost.

For explanation, Figure 9 shows the net imports for the base 39% case as well as the cases with 

additional PV and CSP-TES. The original 39% case results in a severe dip during midday when 

solar output is high, which causes CAISO to reduce imports. Adding PV makes this dip more 

defined, especially in the spring when load is relatively low. CSP-TES also slightly reduces 

imports during the middle of the day, but it also notably reduces the import peak late in the day 

when PV output drops as a result of sunset. The ability of CSP-TES to decrease the evening peak 

is an important source of value to the power system; this is indicated by the higher value for 

CSP-TES shown in Table 8. However, the value of providing dispatchable energy during the 

highest net load hours (net load being defined as system load minus contributions from zeromarginal-cost wind and solar) is only partially captured in the marginal operational value. This 

issue is addressed in Section 5.

Figure 9. Net imports (representing load minus generation in CAISO) for the 33% RPS case, 39% 

RPS case, and the cases with added PV or CSP-TES to reach 40%

3.2 Constraining CAISO Exports 

As mentioned previously, the implications of assuming that other regions can accept RPS energy 

from CAISO represents a large shift in current operations. Therefore, we examine two scenarios 

with export restrictions: capping net hourly exports from CAISO at 1,500 MW and 0 MW. The 

base 39% case with unlimited export ability results in negligible (0.01%) marginal curtailment 

compared to the base 33% case.14 With constrained exports, the marginal curtailment increases 

to 0.1% in the 39% RPS case with 1,500 MW of exports (making overall curtailment 0.02%) and 

1.4% in the case with 0 MW of exports (making overall curtailment 0.1%).15

Table 9 shows the value of 1% additional PV and CSP-TES in reducing operational costs for the 

two limited exports cases. The relative value of CSP-TES over PV rises from $16.4/MWh in the 

 

14 Marginal curtailment represents the curtailment of the incremental resource. Here, the marginal curtailment is the 

percent of added energy curtailed when increasing from 33% to 39%.

15 The export constraint is binding for 80 hours of the year in the 1,500-MW case and 326 hours of the year in the 0-

MW case.
16

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40% case with unlimited exports to $17.4/MWh in the 1,500-MW limit case and $17.7/MWh in 

the 0-MW limit case. The 1,500-MW case shows 1.7% marginal curtailment of incremental PV, 

whereas the 0-MW case shows 7.3% marginal curtailment of the incremental PV.

16 In addition, 

system constraints cause the CSP-TES plant to curtail 0.2% more of its energy in the 1,500-MW 

exports case and 0.6% more energy in the 0-MW exports case compared to the case without 

export constraints. The operational values in Table 9 are normalized by MWh of uncurtailed 

energy (i.e., how much energy the plant would produce before curtailment), which explains in 

part why the value of PV decreases slightly in the cases with constrained exports. This in part 

helps explain the increasing relative value of CSP-TES to PV, because PV curtails more energy 

than does CSP-TES. The value of CSP-TES remains high in the export-constrained case because 

the plant is still able to generate during the highest-cost hours.

Table 9. Marginal Operational Value of PV and CSP-TES in a 40% RPS Case With Export 

Limitations

Base Case 1,500-MW Export Limit 0-MW Export Limit

Marginal Operational

Value ($/MWh) CSP-TES PV CSP-TES PV CSP-TES PV

VO&M 1.5 1.2 1.6 1.2 1.3 0.9

Start-up and Shut-down 2.9 -0.4 3.4 -0.5 3.5 -0.4

Fuel 33.9 25.8 34.6 26.2 35.1 21.6

Emissions 7.9 3.2 8.3 2.8 6.7 1.3

Total 46.2 29.8 47.1 29.7 46.2 28.5

3.3 Sensitivity to Existing Energy Storage

As mentioned previously, we modified the existing CPUC database with the addition of energy 

storage capacity to represent the procurement mandated by the end of 2020 (in accordance with 

Rulemaking R.10-12-007). The presence of this existing storage capacity has an effect on 

subsequent storage value, such as CSP-TES. This value is also dependent on the type of device 

implemented. To demonstrate this effect, we introduced a highly flexible, long-duration battery 

with the ability to immediately switch between charging and discharging. However, Rulemaking 

R.10-12-007 presents guidelines for eligible storage technologies that include short-duration 

storage devices that provide operating reserves and other grid services instead of energy 

arbitrage.17 First, we examine the value-suppressing effect of existing storage on CSP-TES by 

removing the 1,175 MW of storage associated with Rulemaking R.10-12-007. In addition, we 

 

16 Although marginal curtailment is higher in the 0-MW case, both cases still show less than 0.5% curtailment of 

overall RPS energy.

17 The mix of storage types and attributes is expected to evolve in response to utility and state agency 

determinations. In addition, the storage mandate is divided among transmission-interconnected, distributioninterconnected and customer-side storage projects. At present, the CPUC is expecting customer-side projects to act 

as load modifiers but not provide grid services. The distribution-interconnected projects are currently expected to 

have some capability to provide grid services, but the proportion is not yet known. Hence, our decision to use 1,175 

MW of storage as providing grid services is potentially an over-estimate of the actual utilization of these resources, 

and should be understood as one scenario for further refinement.
17

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analyze a scenario in which the 1,175 MW of storage is assumed to operate as a short-duration 

device, providing only ancillary services with no energy-shifting capabilities.

18

To understand the relative value of storage in each of these cases, we first calculate the value 

attributed to the new storage in the 39% case before adding additional CSP-TES or PV. The 

operational value of the new storage capacity is calculated using the same method—attributing 

the reduction in production costs to the new devices. Because the total capacity of the storage 

(1,175 MW) is held constant rather than the amount of energy (or reserves) provided, the value is 

instead normalized by capacity, shown in Table 10. Table 10 also shows the highest system 

value for a co-optimized storage device in which the model is allowed to decide between energy 

arbitrage and holding reserve capacity.

19 The value of the reserves-only device is slightly lower, 

which indicates a high value in holding reserve capacity and allows for a more optimal dispatch 

of the rest of the thermal generator fleet.

Table 10. Annual Operational Benefit of Two Types of Storage Evaluated in the 39% RPS Scenario

1,175 MW of New Storage Annual Operational Benefit of Storage

($/kW storage capacity)

Co-optimized Storage (Base Case) 59.1

Reserves-Only Storage 56.0

Considering the relative benefit that the existing storage capacity above provides to the system, 

we now evaluate the additional CSP-TES or PV generation to reach a 40% RPS. Figure 10

shows the comparative value of CSP-TES and PV added to the system. CSP-TES is worth 

$3.7/MWh more in a system with no additional storage than with the co-optimized storage 

device assumed in the base case. Table 11 shows the relative value of CSP-TES over PV in the 

three cases. The relative values show that as CSP-TES provides more services that are not 

already being performed by the existing storage, its value over PV rises. Note that the value of 

PV and CSP-TES is the highest in the case with no additional storage because storage provides a 

more optimal dispatch of thermal generators that reduces the value of either technology and

lowers the cost of the energy being displaced. 

 

18 This represents a fly-wheel device or similar technology.

19 This scenario is noted as the base case in Table 10 because all the results presented this far have included this 

device.
18

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Figure 10. The operational value of a CSP-TES and PV plant added to a system with varying 

configurations of existing storage

Table 11. Relative Value of CSP-TES Compared to PV Under Various Assumptions Regarding 

Existing Storage

Relative Value of CSP-TES Compared to PV ($/MWh)

Co-optimized Storage (Base) 16.4

Reserves-Only Storage 17.5

No Additional Storage 20.0
19

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4 Impact of Plant Configuration on Value of CSP-TES

The configuration of a CSP-TES plant has significant impact on its ability to provide value to the 

power system (Jorgenson et al. 2013) beyond its interaction with electricity demand patterns and 

penetration of variable generation technologies such as PV. We examine the effect of the 

configuration by varying the size of the power block and thermal storage tank while holding 

constant the size of the solar collection field. As discussed previously, the SM normalizes the 

size of the solar field with respect to the power block. A system with a SM of 1 is sized for the 

solar field to provide the power block with exactly enough energy to operate at its rated capacity 

in reference solar conditions. A larger SM implies a larger solar collector area. For example, 

Figure 11 depicts two days of solar inflow for a CSP plant with a power block rating of 300 MW 

and a SM of 2. Any electrical energy delivered from the solar field that exceeds the maximum 

thermal rating of the power block must be stored—or curtailed for systems without storage.20 As 

the diagram indicates, excess energy (in yellow) from an oversized field (a SM greater than 1) 

can be sent to thermal storage and subsequently delivered to the power block, resulting in a 

higher plant capacity factor. However, plant configurations with a very high capacity factor

produce a fairly flat output. This requires a large fraction of solar energy to be stored during the

day when electricity costs are the highest and then discharged during off-peak periods of low 

value. Sioshansi and Denholm previously analyzed different configurations using a simple 

“price-taker” model and demonstrated how lower SMs may provide the highest value 

considering the time-varying value of electricity (2010). Detailed dispatch modeling in a small 

test system supported this idea (Jorgenson et al. 2013).

Figure 11. Impact of the solar multiple parameter on the energy flow of a CSP plant

To evaluate the operational value of different SMs, we change the rated capacity of the power 

block instead of the size of the solar field. Therefore, the electrical equivalent solar energy 

available for use in each scenario remains constant, but the rated capacity of the power block 

 

20 Energy from CSP plants may be curtailed by defocusing some of the mirrors and reducing the amount of thermal 

energy delivered from the solar field.
20

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varies. We also increase the storage capacity to quantify the additional system benefit provided 

by increased storage capacity. 

Table 12 shows the studied parameters. As previously described, we calculate the system 

benefits by calculating the avoided production costs as a direct result of adding the CSP-TES 

plant to the system discussed earlier, and then we normalize by the amount of energy produced, 

shown in Figure 12.

Table 12. The SMs and Corresponding Plant Characteristics of CSP-TES Plants Modeled

Solar Multiple Rated Capacity 

of Plant (MW)

Electrical Equivalent Inflow 

from Field (GWh)

Hours of TES 

Capacity Tested21

1.3 1,172 3,667 0, 3, 6

1.7 896 3,667 3, 6, 9

2 762 3,667 6, 9, 12

2.3 663 3,667 9, 12, 15

2.7 564 3,667 12, 15

Figure 12 indicates two trends that have been previously reported (Jorgenson 2013). First, the 

value of CSP-TES energy per unit produced generally increases with thermal energy storage 

capacity. Each of these plants has a SM greater than 1, meaning that the solar field is oversized 

compared to the power block. Therefore, during hours with high insolation and with insufficient 

(or no) thermal storage capacity, some solar energy must be curtailed. Increasing the storage 

capacity reduces the amount of energy that must be curtailed. In addition, thermal energy storage 

allows the energy to be shifted to periods with higher energy prices. Figure 13 shows the 

marginal energy price in the base case 40% RPS case on an average day during three seasons in 

CAISO as modeled. The highest-priced hours occur in the evening after or during sunset. Figure 

14, which shows the average daily dispatch of CSP-TES during different seasons, indicates the 

time-shifting ability of TES. With no storage, the plant must be dispatched during the day. With 

increasing energy storage, the plant dispatch follows CAISO’s marginal energy price. 

Figure 12. Marginal operational value of tower CSP-TES plants in the 40% RPS scenario with 

varying configurations

 

21 Generally, storage capacity is measured in terms of hours of output at rated capacity.
21

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Figure 13. Average daily marginal energy prices for three major seasons in the 40% RPS scenario 

in CAISO

Figure 14. Average daily dispatch of a tower CSP-TES plant in a CAISO 40% RPS scenario with a 

SM of 1.3 and with 0, 3, and 6 hours of TES

Second, Figure 12 shows that the marginal value of plants with lower solar multiples is generally 

higher than plants with higher SMs per unit energy delivered. Because power block size 

decreases as SM increases, plants with higher SMs are forced to store an increasing fraction of 

solar energy, even during periods of high energy prices. In this case, the size of the power block 

limits the amount of energy that can be generated during the highest-priced hours of the day, 

which generally occur after sundown. Figure 15 shows the average daily dispatch for three CSPTES plants with six hours of thermal storage capacity and varying SMs. As SM increases, the 

plant is forced to flatten its output and behave more like a baseload power plant that exhibits a 

relatively flat output profile. Because they are dispatchable over additional hours, the plants with 

smaller SMs act as peaking plants that displace more expensive generation sources during the 

highest-priced hours.
22

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Figure 15. Average daily dispatch of a tower CSP-TES plant in CAISO with 6 hours of TES capacity 

and SMs of 1.3, 1.7, and 2.0

Figure 16 further explains the trends shown in Figure 12 by showing the restrictions on incoming 

solar energy. The incoming energy can be divided into three categories, before any losses: (1) 

energy that must go directly to the power block, (2) energy that must be stored, and (3) 

dispatchable energy. The first category represents energy that cannot be stored because the 

storage tank is finite in size. The second category arises from the fact that all plants modeled here 

have an undersized power block relative to the solar field. During high insolation, the solar field 

will collect more energy than can be used in that hour, which results in energy that must be sent 

to storage or be curtailed if the tank is full. The rest of the energy is classified as dispatchable 

because the model can choose to either send it to storage or generate power immediately. Plants 

with more dispatchable energy provide the most benefit to the system because fewer constraints 

on the incoming energy allow its output to be more fully optimized.

Figure 16. Breakdown of the limitations of incoming solar energy for solar multiples varying 

(across) from 1.3 to 2.7 with increasing amounts of thermal storage
23

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5 Impact of Capacity on the Overall Value of CSP-TES

The results of this analysis indicate that CSP-TES has a higher marginal operational value than 

PV, and that the relative value may increase slightly with increased PV penetration. However, 

calculating the avoided operational costs captures only one source of value. Next, we examine 

another source of potential value: firm capacity. The capacity value reflects the ability of PV or 

CSP-TES to avoid the costs of building new conventional thermal generators to meet demand. 

Note that firm capacity has value only in systems that need capacity to respond to growing 

energy demand or plant retirements. Capacity value in overbuilt systems with large amounts of 

excess capacity is essentially zero. 

In this system, we add either 1,172 MW of CSP-TES or 1,576 MW of PV. However, these are 

the plants’ rated, or nameplate, capacity, and do not necessarily reflect the generator’s 

availability during periods of peak demand. The actual effective load-carrying capability (ELCC) 

for a generator is the amount of load that could be added to the system with the presence of a 

new generator while still meeting a reliability target. Historically, the reliability is measured 

using a loss-of-load expectation metric and a targeted reliability such as of 1 day of unserved 

energy every 10 years. 

To estimate the capacity value of PV, we used the Renewable Energy Probabilistic Resource 

Adequacy tool (REPRA) tool that conducts a full ELCC calculation to determine how nondispatchable sources of power such as PV can contribute to the reliability of a power system 

(Ibanez and Milligan 2012, Sigrin et al. 2014). This model uses the effective forced-outage rate 

for conventional plants and profiles of renewable generators to calculate loss-of-load probability 

(LOLP) and ultimately the ELCC of additional generation types such as solar PV.

The application of the REPRA tool to the additional PV plant in the 33% RPS and the CAISO 

projected load for 2022, with an overall PV penetration of 10.9%, gives an ELCC of 346 MW for 

the incremental PV (which is equal to a capacity credit of 22.0% given the plant capacity of 

1,576 MW). The 40% RPS case, with an overall PV penetration of 14.1% in CAISO, gives an 

ELCC of 53.2 MW for the incremental PV (which is equal to a capacity credit of 3.4% given the 

plant capacity of 1,576 MW). Note that high PV penetration continues to shift the daily peak net 

load until later in the evening, when PV output drops. The hours with the highest LOLP 

generally occur during hours of highest net load. This indicates that as PV penetration increases, 

PV is less able to contribute in the peak net load hours. This very low value may be the result of 

the single year of data or other uncertainties in the data set. Because of this, we use a range of 

estimates with an upper value derived from Mills and Wiser, who calculated an approximate 

30% capacity credit for a PV penetration of 10.9% and a 20% capacity credit for a PV 

penetration of 14.1% (2012a, 2012b).

22

The traditional ELCC calculation becomes more complicated for CSP-TES because of the 

presence of dispatchable thermal storage. Similar to PV, we use an approximation method for 

estimating capacity credit for CSP-TES and thus the actual capacity value that a plant may earn. 

First, we examine the solar resource and dispatch of the CSP-TES plant during the hours with the 

 

22 This high value includes single-axis tracking with latitude tilt.
24

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

highest net load, which is used as a proxy for the hours with the highest LOLP.

23 The actual 

contribution of CSP-TES to the load-carrying capacity of the system should include not only its 

output during the 100 hours with the highest net load but also the availability of thermal energy 

to increase its output upward during those hours, as discussed by Tuohy and O’Malley (2011). 

For example, if the CSP-TES plant was dispatched below its full capacity during hours with high 

net load but has enough energy stored to increase its output, then it would receive a full capacity 

credit in this method. However, if the CSP-TES plant were forced to increase its generation over 

subsequent high-load hours, the available output could not exceed the thermal energy in reserve. 

That is, the calculation must reflect the energy limitations on the CSP-TES plant. The base 

configuration CSP-TES plant (with a SM of 1.3 and six hours of TES) earns a capacity credit of 

92.8% in the 33% RPS case and a credit of 96.6% in the 40% RPS case using this method. This 

counterintuitive result in which the capacity credit of CSP-TES increases at a higher solar 

penetration is due to the “narrowing” of the daily peak resulting from the increased penetration 

of solar PV, demonstrated further in Appendix B. This complicates the allocation of capacity 

credit to differing technologies, because although PV appears to enable a slightly increased 

capacity credit of CSP, it receives very low capacity credit due to its non-coincidence with the 

shifted peak. It also demonstrates another element of the potential co-benefits of deploying both 

solar technologies in high renewable scenarios.24

Applying the annualized capacity cost of a new combustion turbine in California ($150/kW-y or

$190/kW-y)25 to the effective capacity of each plant, or the rated capacity adjusted by the 

capacity credit, and then normalizing by the amount of energy produced leads to a range of 

capacity values measured in terms of $/MWh. For PV, the capacity value is $15.2/MWh to 

$26.3/MWh in the 33% case and $2.36/MWh to $17.6/MWh in the 40% case26. For CSP-TES, 

the capacity value is $47.9/MWh to $60.8/MWh in the 33% case and $49.8/MW/h to 

$63.1/MWh in the 40% case. To quantify the total value of each technology, we add the 

operational value (presented in Section 3) and the range of capacity values. This results in a 

range of total values for PV and CSP-TES in the two RPS scenarios, depicted in Table 13 and 

Figure 17.

 

23 For more information about this approach and its limitations, see Sioshansi et al. (2013). 24 Another benefit not analyzed here is the ability of CSP plants to replace less flexible units with higher minimum 

turndown ratios. This would enable greater penetration of PV by reducing overgeneration events, as discussed by 

Denholm and Mehos (2011).

25 Lower estimate from Pfeifenberger 2013, upper estimate from CAISO 2012. 26 The range of capacity values reflects the low capacity credit and low capacity value bound and the high capacity 

credit and high capacity value bound.
25 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 13. Total Value (Operational Value Plus Capacity Value) Range in Two RPS Scenarios

33% RPS

Lower Estimate

($/MWh)

Upper Estimate

($/MWh)

PV 47.1 58.2

CSP-TES 94.6 107

40% RPS

Lower Estimate

($/MWh)

Upper Estimate

($/MWh)

PV 32.2 47.4

CSP-TES 96.0 109

Figure 17. Total value range, which includes operational and capacity value, of CSP-TES and PV in

two RPS scenarios

Applying the annualized capacity cost ($150/kW-y or $190/kW-y) to the various configurations 

of CSP-TES plants in the 40% RPS, as discussed in Section 4, leads to different overall values 

for various configurations. First, we calculate capacity credit as discussed above, shown in Table 

14. The plants with higher solar multiples have higher capacity credits because of their surplus of 

solar energy relative to rated capacity, and plants with more thermal storage capacity have higher 

capacity credits because of their ability to shift solar energy to the hours of highest net load. 

However, combining operational value with total value indicates that low SM plants (with 

thermal storage) have the highest overall value but also the largest ranges for overall value. This 

is shown in Figure 18. 
26 

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Table 14. Capacity Credit Assigned to Various Configurations of CSP-TES Plants in the 40% RPS 

Scenario Using Two Methods

Capacity Credit Including 

Dispatch and Useable Storage

Hours of 

TES

SM

1.3 1.7 2 2.3 2.7

0 0.092

3 0.946 0.979

6 0.966 0.989 0.999

9 0.995 1.00 1.00

12 1.00 1.00 1.00

15 1.00 1.00

Figure 18. Total operational and capacity value of several configurations of CSP-TES in the 40% 

RPS scenario
27

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

6 Conclusions

We evaluated the relative value of PV and CSP-TES in the California grid system with two 

levels of assumed RPS-mandated renewable penetrations: 33% and 40%, which have PV 

penetrations of 10.9% and 14.1%, respectively. In the 33% case, we found an operational value 

of $46.6/MWh for our base CSP-TES configuration and $31.9/MWh for PV. These values are 

lower than a previous 33% RPS study based largely on the assumed reduction in cost of natural 

gas and CO2 emissions. In the 40% case, we estimated an operational value of $46.2/MWh for 

CSP and $29.8/MWh for PV. Several sensitivities to these cases were also evaluated. Larger 

CSP solar multiples tend to reduce the value of CSP because the plant produces a more base-load 

output, producing more energy during times of lower value. However, plants with smaller solar 

multiples may be significantly more expensive to build per unit of energy produced; this

warrants future analysis. Reducing export capacity increased PV curtailment somewhat, slightly 

lowering its value to the system. Eliminating 1,175 MW of energy storage increases the 

flexibility benefits of dispatchable CSP and results in higher value.

The value of CSP appears to be largely derived from its ability to provide firm system capacity. 

Depending on the assumptions used, the capacity value (expressed in terms of $/MWh of 

generation) for the CSP plant was estimated from $47.9/MWh to $60.8/MWh in the 33% case 

and $49.8/MWh to $63.1/MWh in the 40% case. This produced a total value for the base CSP 

configuration of $94.6/MWh to $107/MWh in the 33% case and $96.0/MWh to $109/MWh in 

the 40% case. Although we observed a relatively small drop in CSP value between the scenarios, 

a greater reduction was observed for PV, largely because of the reduction in capacity value. We 

estimated total values for PV from $47.1/MWh to $58.2/MWh in the 33% case and $32.2/MWh 

to 47.4/MWh in the 40% case. This produced a relative value of CSP of about $47/MWh to 

$49/MWh greater than PV in the 33% case and $62/MWh to $64/MWh greater in the 40% case. 

The steep decline in capacity value for PV has been observed before, but the results presented 

here utilized only one year of data. Because the value of both PV and CSP are highly dependent 

on their ability to provide firm capacity, a more robust capacity value calculation could provide 

valuable insight. In addition, further analysis will determine the system needs at shorter 

timescales as VG integration continues to influence sub-hourly system operations. Finally, this 

analysis compared only the relative value of CSP-TES in multiple scenarios and configurations 

and did not consider upfront cost. The capital costs are highly dependent on many assumptions, 

including the solar resource and achievement of cost-reduction goals. Future work should

analyze the trade-off between value to the grid and required costs. 
28

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7 References

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31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix A: Changes in CSP Value Relative to the 

2013 CSP Value Study

As reported in Section 2.2, the operational value of CSP-TES in the 33% RPS case, $46.6/MWh, 

is lower than previously reported values in the same database. Denholm et al. reported an 

operational value of $83/MWh (2013). This produces a decrease in value of approximately 

$36.4/MWh. Table A-1 summarizes the sources of this difference.

Table A-1. Sources of Value Reduction in This Study Compared to a Previous NREL Study of CSP 

in CAISO

Change

Decrease in 

Value in This 

Study

Reduced natural gas prices. This study assumes an average of $4.4/MMBtu

compared to the assumptions in the previous iteration of the database (between $5.6 

and $6.3/MMBtu). This results in a $13/MWh to $15/MWh direct difference and acts as 

a multiplier on several other costs.

$13–$15

Reduced CO2 costs. This study assumes that a cost of CO2 is $22/ton in California 

and $0/ton outside of California, down from $36/ton in all of the Western 

Interconnection in the previous analysis. 

$10–$12

Reduced reserve provision due to the introduction of storage. This issue is 

discussed in Section 3.3 and produces a difference of $4/MWh to $5/MWh after 

considering the impact of fuel prices and CO2 costs.

$5–$6

More conservative CSP assumptions. This study includes more realistic (and 

conservative) part-load heat rate and start costs for CSP-TES plants. $2–$4

General database updates. This includes assumptions about VO&M costs of 

conventional generators, heat rates, ramp rates, and other operational parameters and 

costs.

$3–$4

Use of tower plant. The previous study used a trough plant. For more explanation 

about the difference in operational value, see Jorgenson et al. (2013). $2–3

Use of reduced solar multiple. The operational value presented here analyzes a SM 

of 1.3, whereas the previous analysis utilized a SM of 2. For more information about the 

drivers of this deviation, see Section 4.

-$6 to -$7

Total $29–$38
32

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix B: How PV Can Increase the Capacity Value 

of CSP-TES

Section 5 demonstrates a slight increase in capacity credit for the CSP plant when increasing the 

RPS penetration from 33% to 40%. This result appears counter to previous analysis that 

demonstrated a decreasing capacity credit for PV or CSP-TES when analyzed independently 

(Mills and Wiser 2012a, 2012b). The explanation for our counterintuitive result is based on the 

fact that we are considering the combined impact of both PV and CSP. Figure A-1 illustrates the 

load and contribution from PV and wind (labeled as RE Gen) to net load for July 22 in both the 

33% and 39% scenarios. This is one of the days of peak net demand that drives the capacity 

credit measurement.

The 40% scenario demonstrates two important factors. First, the addition of PV effectively shifts 

the hour of peak demand on this day from 4 p.m. to 6 p.m. During this hour, the capacity credit 

of any incremental PV is very small. However, the large amount of PV also reduces the duration 

(number of hours) of the peak period, meaning fewer hours of storage may be needed to meet 

this shorter peak. In the 33% case, the natural inflow of solar energy and 6 hours of storage 

capability is enough to cover approximately 10 of the 12 peak hours in this period, or run at 

approximately 85% of rated capacity during the entire peak period. However, in the 39% case, 

the reduced peak duration allows the plant to operate at rated capacity during approximately 9.5 

of the 10 hours at full capacity, or approximately 95% of rated capacity during the entire period. 

Overall, this issue demonstrates the complementary nature of PV and CSP and adds difficulty in 

“allocating” the value of different technologies. 

Figure A-1. Change in peak demand period resulting from the deployment of PV on July 22